{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Keras tutorial - the Happy House\n",
    "\n",
    "欢迎来到第2周的第一个任务。在这个任务中，您将：\n",
    "1. 学习使用Keras，这是一个用Python编写的高级神经网络API（编程框架），能够在包括TensorFlow和CNTK在内的几个底层框架上运行。\n",
    "2. 看看如何在几个小时内建立一个深度学习算法。\n",
    "\n",
    "为什么我们使用Keras？Keras的开发是为了让深度学习的工程师能够非常快速地建立和尝试不同的模型。就像TensorFlow是一个比Python更高级别的框架一样，Keras是一个更高层次的框架，并提供额外的抽象。能够以最少的时间根据ideal获取结果是寻找好模型的关键。可是，Keras相对底层框架拥有更多的限制，所以有些能在TensorFlow中实现的非常复杂的模型在Keras中却不能实现。也就是说，Keras 只对常见的模型支持良好。\n",
    "\n",
    "在本次练习中，你将处理“Happy House”相关的问题，这个我们将在下面解释。让我们先加载所需的软件包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from keras import layers\n",
    "from keras.layers import Input, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D\n",
    "from keras.layers import AveragePooling2D, MaxPooling2D, Dropout, GlobalMaxPooling2D, GlobalAveragePooling2D\n",
    "from keras.models import Model\n",
    "from keras.preprocessing import image\n",
    "from keras.utils import layer_utils\n",
    "from keras.utils.data_utils import get_file\n",
    "from keras.applications.imagenet_utils import preprocess_input\n",
    "import pydot\n",
    "from IPython.display import SVG\n",
    "from keras.utils.vis_utils import model_to_dot\n",
    "from keras.utils import plot_model\n",
    "from kt_utils import *\n",
    "\n",
    "import keras.backend as K\n",
    "K.set_image_data_format('channels_last')\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import imshow\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: 正如你所看到的，我们从Keras中导入了很多功能。只需在笔记本中直接调用即可使用. Ex: `X = Input(...)` or `X = ZeroPadding2D(...)`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 - The Happy House \n",
    "\n",
    "For your next vacation, you decided to spend a week with five of your friends from school. It is a very convenient house with many things to do nearby. But the most important benefit is that everybody has commited to be happy when they are in the house. 任何想要进入房子的人都必须证明自己当前是开心快乐的。\n",
    "\n",
    "<img src=\"images/happy-house.jpg\" style=\"width:350px;height:270px;\">\n",
    "<caption><center> <u> <font color='purple'> **Figure 1** </u><font color='purple'>  : **the Happy House**</center></caption>\n",
    "\n",
    "\n",
    "作为一名深度学习专家，为了确保 \"Happy\" rule 得到严格实施, 您将打算使用前门摄像头拍摄的图片来检查该人是否快乐。只有当人感到快乐时，门才会打开。\n",
    "\n",
    "你收集了你的朋友和你自己的照片，由前门摄像头拍摄。数据集是带有标签的。\n",
    "\n",
    "<img src=\"images/house-members.png\" style=\"width:550px;height:250px;\">\n",
    "\n",
    "运行下面的代码来标准化数据集并了解它的形状。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of training examples = 600\n",
      "number of test examples = 150\n",
      "X_train shape: (600, 64, 64, 3)\n",
      "Y_train shape: (600, 1)\n",
      "X_test shape: (150, 64, 64, 3)\n",
      "Y_test shape: (150, 1)\n"
     ]
    }
   ],
   "source": [
    "X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = load_dataset()\n",
    "\n",
    "# Normalize image vectors\n",
    "X_train = X_train_orig/255.\n",
    "X_test = X_test_orig/255.\n",
    "\n",
    "# Reshape\n",
    "Y_train = Y_train_orig.T\n",
    "Y_test = Y_test_orig.T\n",
    "\n",
    "print (\"number of training examples = \" + str(X_train.shape[0]))\n",
    "print (\"number of test examples = \" + str(X_test.shape[0]))\n",
    "print (\"X_train shape: \" + str(X_train.shape))\n",
    "print (\"Y_train shape: \" + str(Y_train.shape))\n",
    "print (\"X_test shape: \" + str(X_test.shape))\n",
    "print (\"Y_test shape: \" + str(Y_test.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**“Happy”数据集的详细信息：**:\n",
    "- Images are of shape (64,64,3)\n",
    "- Training: 600 pictures\n",
    "- Test: 150 pictures\n",
    "\n",
    "It is now time to solve the \"Happy\" Challenge."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 - Building a model in Keras\n",
    "\n",
    "Keras 非常适合快速制作原型。在很短的时间内，你就能够构建一个模型，并获得出色的成果。\n",
    "\n",
    "Here is an example of a model in Keras:\n",
    "\n",
    "```python\n",
    "def model(input_shape):\n",
    "    # Define the input placeholder as a tensor with shape input_shape. Think of this as your input image!\n",
    "    X_input = Input(input_shape)\n",
    "\n",
    "    # Zero-Padding: pads the border of X_input with zeroes\n",
    "    X = ZeroPadding2D((3, 3))(X_input)\n",
    "\n",
    "    # CONV -> BN -> RELU Block applied to X\n",
    "    X = Conv2D(32, (7, 7), strides = (1, 1), name = 'conv0')(X)\n",
    "    X = BatchNormalization(axis = 3, name = 'bn0')(X)\n",
    "    X = Activation('relu')(X)\n",
    "\n",
    "    # MAXPOOL\n",
    "    X = MaxPooling2D((2, 2), name='max_pool')(X)\n",
    "\n",
    "    # FLATTEN X (means convert it to a vector) + FULLYCONNECTED\n",
    "    X = Flatten()(X)\n",
    "    X = Dense(1, activation='sigmoid', name='fc')(X)\n",
    "\n",
    "    # Create model. This creates your Keras model instance, you'll use this instance to train/test the model.\n",
    "    model = Model(inputs = X_input, outputs = X, name='HappyModel')\n",
    "    \n",
    "    return model\n",
    "```\n",
    "\n",
    "Note that Keras uses a different convention with variable names than we've previously used with numpy and TensorFlow. In particular, rather than creating and assigning a new variable on each step of forward propagation such as `X`, `Z1`, `A1`, `Z2`, `A2`, etc. for the computations for the different layers, in Keras code each line above just reassigns `X` to a new value using `X = ...`. In other words, during each step of forward propagation, we are just writing the latest value in the commputation into the same variable `X`. The only exception was `X_input`, which we kept separate and did not overwrite, since we needed it at the end to create the Keras model instance (`model = Model(inputs = X_input, ...)` above). \n",
    "\n",
    "**Exercise**: Implement a `HappyModel()`. This assignment is more open-ended than most. We suggest that you start by implementing a model using the architecture we suggest, and run through the rest of this assignment using that as your initial model. But after that, come back and take initiative to try out other model architectures. For example, you might take inspiration from the model above, but then vary the network architecture and hyperparameters however you wish. You can also use other functions such as `AveragePooling2D()`, `GlobalMaxPooling2D()`, `Dropout()`. \n",
    "\n",
    "**Note**: You have to be careful with your data's shapes. Use what you've learned in the videos to make sure your convolutional, pooling and fully-connected layers are adapted to the volumes you're applying it to."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: HappyModel\n",
    "\n",
    "def HappyModel(input_shape):\n",
    "    \"\"\"\n",
    "    Implementation of the HappyModel.\n",
    "    \n",
    "    Arguments:\n",
    "    input_shape -- shape of the images of the dataset\n",
    "\n",
    "    Returns:\n",
    "    model -- a Model() instance in Keras\n",
    "    \"\"\"\n",
    "    \n",
    "    ### START CODE HERE ###\n",
    "    # Feel free to use the suggested outline in the text above to get started, and run through the whole\n",
    "    \n",
    "    # Define the input placeholder as a tensor with shape input_shape. Think of this as your input image!\n",
    "    X_input = Input(input_shape)\n",
    "\n",
    "    # Zero-Padding: pads the border of X_input with zeroes\n",
    "    X = ZeroPadding2D((3, 3))(X_input)\n",
    "\n",
    "    # CONV -> BN -> RELU Block applied to X\n",
    "    X = Conv2D(32, (7, 7), strides = (1, 1), name = 'conv0')(X)\n",
    "    X = BatchNormalization(axis = 3, name = 'bn0')(X)\n",
    "    X = Activation('relu')(X)\n",
    "\n",
    "    # MAXPOOL\n",
    "    X = MaxPooling2D((2, 2), name='max_pool')(X)\n",
    "\n",
    "    # FLATTEN X (means convert it to a vector) + FULLYCONNECTED\n",
    "    X = Flatten()(X)\n",
    "    X = Dense(1, activation='sigmoid', name='fc')(X)\n",
    "\n",
    "    # Create model. This creates your Keras model instance, you'll use this instance to train/test the model.\n",
    "    model = Model(inputs = X_input, outputs = X, name='HappyModel')\n",
    "    # exercise (including the later portions of this notebook) once. The come back also try out other\n",
    "    # network architectures as well. \n",
    "    \n",
    "    \n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You have now built a function to describe your model. To train and test this model, there are four steps in Keras:\n",
    "1. Create the model by calling the function above\n",
    "2. Compile the model by calling `model.compile(optimizer = \"...\", loss = \"...\", metrics = [\"accuracy\"])`\n",
    "3. Train the model on train data by calling `model.fit(x = ..., y = ..., epochs = ..., batch_size = ...)`\n",
    "4. Test the model on test data by calling `model.evaluate(x = ..., y = ...)`\n",
    "\n",
    "If you want to know more about `model.compile()`, `model.fit()`, `model.evaluate()` and their arguments, refer to the official [Keras documentation](https://keras.io/models/model/).\n",
    "\n",
    "**Exercise**: Implement step 1, i.e. create the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "### START CODE HERE ### (1 line)\n",
    "happyModel = HappyModel(X_train.shape[1:])\n",
    "### END CODE HERE ###"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise**: Implement step 2, i.e. compile the model to configure the learning process. Choose the 3 arguments of `compile()` wisely. Hint: the Happy Challenge is a binary classification problem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "### START CODE HERE ### (1 line)\n",
    "happyModel.compile(optimizer = \"Adam\", loss = \"binary_crossentropy\", metrics = [\"accuracy\"])\n",
    "### END CODE HERE ###"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise**: Implement step 3, i.e. train the model. Choose the number of epochs and the batch size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/39\n",
      "600/600 [==============================] - 26s - loss: 1.5768 - acc: 0.7183    \n",
      "Epoch 2/39\n",
      "600/600 [==============================] - 26s - loss: 0.3022 - acc: 0.8850    \n",
      "Epoch 3/39\n",
      "600/600 [==============================] - 25s - loss: 0.1880 - acc: 0.9183    \n",
      "Epoch 4/39\n",
      "600/600 [==============================] - 25s - loss: 0.1269 - acc: 0.9567    \n",
      "Epoch 5/39\n",
      "600/600 [==============================] - 25s - loss: 0.1374 - acc: 0.9433    \n",
      "Epoch 6/39\n",
      "600/600 [==============================] - 26s - loss: 0.1624 - acc: 0.9550    \n",
      "Epoch 7/39\n",
      "600/600 [==============================] - 27s - loss: 0.0701 - acc: 0.9767    \n",
      "Epoch 8/39\n",
      "600/600 [==============================] - 26s - loss: 0.1520 - acc: 0.9417    \n",
      "Epoch 9/39\n",
      "600/600 [==============================] - 25s - loss: 0.1620 - acc: 0.9483    \n",
      "Epoch 10/39\n",
      "600/600 [==============================] - 25s - loss: 0.1383 - acc: 0.9467    \n",
      "Epoch 11/39\n",
      "600/600 [==============================] - 25s - loss: 0.1588 - acc: 0.9517    \n",
      "Epoch 12/39\n",
      "600/600 [==============================] - 25s - loss: 0.1038 - acc: 0.9683    \n",
      "Epoch 13/39\n",
      "600/600 [==============================] - 25s - loss: 0.0895 - acc: 0.9683    \n",
      "Epoch 14/39\n",
      "600/600 [==============================] - 25s - loss: 0.0828 - acc: 0.9717    \n",
      "Epoch 15/39\n",
      "600/600 [==============================] - 25s - loss: 0.0456 - acc: 0.9917    \n",
      "Epoch 16/39\n",
      "600/600 [==============================] - 26s - loss: 0.0616 - acc: 0.9800    \n",
      "Epoch 17/39\n",
      "600/600 [==============================] - 25s - loss: 0.0463 - acc: 0.9817    \n",
      "Epoch 18/39\n",
      "600/600 [==============================] - 28s - loss: 0.0772 - acc: 0.9683    \n",
      "Epoch 19/39\n",
      "600/600 [==============================] - 28s - loss: 0.0486 - acc: 0.9850    \n",
      "Epoch 20/39\n",
      "600/600 [==============================] - 26s - loss: 0.0235 - acc: 0.9900    \n",
      "Epoch 21/39\n",
      "600/600 [==============================] - 25s - loss: 0.0403 - acc: 0.9933    \n",
      "Epoch 22/39\n",
      "600/600 [==============================] - 25s - loss: 0.0554 - acc: 0.9850    \n",
      "Epoch 23/39\n",
      "600/600 [==============================] - 25s - loss: 0.0221 - acc: 0.9967    \n",
      "Epoch 24/39\n",
      "600/600 [==============================] - 25s - loss: 0.0430 - acc: 0.9800    \n",
      "Epoch 25/39\n",
      "600/600 [==============================] - 25s - loss: 0.0286 - acc: 0.9967    \n",
      "Epoch 26/39\n",
      "600/600 [==============================] - 25s - loss: 0.0245 - acc: 0.9917    \n",
      "Epoch 27/39\n",
      "600/600 [==============================] - 25s - loss: 0.0168 - acc: 0.9933    \n",
      "Epoch 28/39\n",
      "600/600 [==============================] - 25s - loss: 0.0414 - acc: 0.9867    \n",
      "Epoch 29/39\n",
      "600/600 [==============================] - 25s - loss: 0.0281 - acc: 0.9900    \n",
      "Epoch 30/39\n",
      "600/600 [==============================] - 26s - loss: 0.0445 - acc: 0.9883    \n",
      "Epoch 31/39\n",
      "600/600 [==============================] - 25s - loss: 0.0693 - acc: 0.9733    \n",
      "Epoch 32/39\n",
      "600/600 [==============================] - 25s - loss: 0.1834 - acc: 0.9383    \n",
      "Epoch 33/39\n",
      "600/600 [==============================] - 25s - loss: 0.3500 - acc: 0.9150    \n",
      "Epoch 34/39\n",
      "600/600 [==============================] - 25s - loss: 0.0899 - acc: 0.9683    \n",
      "Epoch 35/39\n",
      "600/600 [==============================] - 26s - loss: 0.1298 - acc: 0.9717    \n",
      "Epoch 36/39\n",
      "600/600 [==============================] - 26s - loss: 0.1342 - acc: 0.9667    \n",
      "Epoch 37/39\n",
      "600/600 [==============================] - 25s - loss: 0.0550 - acc: 0.9767    \n",
      "Epoch 38/39\n",
      "600/600 [==============================] - 25s - loss: 0.0812 - acc: 0.9767    \n",
      "Epoch 39/39\n",
      "600/600 [==============================] - 25s - loss: 0.0726 - acc: 0.9867    \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0xeae0c18>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### START CODE HERE ### (1 line)\n",
    "happyModel.fit(x=X_train, y = Y_train, epochs = 39, batch_size = 16)\n",
    "### END CODE HERE ###"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that if you run `fit()` again, the `model` will continue to train with the parameters it has already learnt instead of reinitializing them.\n",
    "\n",
    "**Exercise**: Implement step 4, i.e. test/evaluate the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "150/150 [==============================] - 3s     \n",
      "\n",
      "Loss = 0.596558374564\n",
      "Test Accuracy = 0.846666665872\n"
     ]
    }
   ],
   "source": [
    "### START CODE HERE ### (1 line)\n",
    "preds = happyModel.evaluate(x = X_test, y = Y_test)\n",
    "### END CODE HERE ###\n",
    "print()\n",
    "print (\"Loss = \" + str(preds[0]))\n",
    "print (\"Test Accuracy = \" + str(preds[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If your `happyModel()` function worked, you should have observed much better than random-guessing (50%) accuracy on the train and test sets.\n",
    "\n",
    "To give you a point of comparison, our model gets around **95% test accuracy in 40 epochs** (and 99% train accuracy) with a mini batch size of 16 and \"adam\" optimizer. But our model gets decent accuracy after just 2-5 epochs, so if you're comparing different models you can also train a variety of models on just a few epochs and see how they compare. \n",
    "\n",
    "If you have not yet achieved a very good accuracy (let's say more than 80%), here're some things you can play around with to try to achieve it:\n",
    "\n",
    "- Try using blocks of CONV->BATCHNORM->RELU such as:\n",
    "```python\n",
    "X = Conv2D(32, (3, 3), strides = (1, 1), name = 'conv0')(X)\n",
    "X = BatchNormalization(axis = 3, name = 'bn0')(X)\n",
    "X = Activation('relu')(X)\n",
    "```\n",
    "until your height and width dimensions are quite low and your number of channels quite large (≈32 for example). You are encoding useful information in a volume with a lot of channels. You can then flatten the volume and use a fully-connected layer.\n",
    "- You can use MAXPOOL after such blocks. It will help you lower the dimension in height and width.\n",
    "- Change your optimizer. We find Adam works well. \n",
    "- If the model is struggling to run and you get memory issues, lower your batch_size (12 is usually a good compromise)\n",
    "- Run on more epochs, until you see the train accuracy plateauing. \n",
    "\n",
    "Even if you have achieved a good accuracy, please feel free to keep playing with your model to try to get even better results. \n",
    "\n",
    "**Note**: If you perform hyperparameter tuning on your model, the test set actually becomes a dev set, and your model might end up overfitting to the test (dev) set. But just for the purpose of this assignment, we won't worry about that here.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 - Conclusion\n",
    "\n",
    "Congratulations, you have solved the Happy House challenge! \n",
    "\n",
    "Now, you just need to link this model to the front-door camera of your house. We unfortunately won't go into the details of how to do that here. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='blue'>\n",
    "**What we would like you to remember from this assignment:**\n",
    "- Keras is a tool we recommend for rapid prototyping. It allows you to quickly try out different model architectures. Are there any applications of deep learning to your daily life that you'd like to implement using Keras? \n",
    "- Remember how to code a model in Keras and the four steps leading to the evaluation of your model on the test set. Create->Compile->Fit/Train->Evaluate/Test."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4 - Test with your own image (Optional)\n",
    "\n",
    "Congratulations on finishing this assignment. You can now take a picture of your face and see if you could enter the Happy House. To do that:\n",
    "    1. Click on \"File\" in the upper bar of this notebook, then click \"Open\" to go on your Coursera Hub.\n",
    "    2. Add your image to this Jupyter Notebook's directory, in the \"images\" folder\n",
    "    3. Write your image's name in the following code\n",
    "    4. Run the code and check if the algorithm is right (0 is unhappy, 1 is happy)!\n",
    "    \n",
    "The training/test sets were quite similar; for example, all the pictures were taken against the same background (since a front door camera is always mounted in the same position). This makes the problem easier, but a model trained on this data may or may not work on your own data. But feel free to give it a try! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  8.44492405e-08]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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gYVIlK85KMvko/x4NRQIAmKL42qlUF9pIVdv0adh2evfB/WzcyHFoDtfXcXP4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\nF7+GRori/wOMhrX8XuEH+AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x138072e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### START CODE HERE ###\n",
    "img_path = 'images/my_image.jpg'\n",
    "### END CODE HERE ###\n",
    "img = image.load_img(img_path, target_size=(64, 64))\n",
    "imshow(img)\n",
    "\n",
    "x = image.img_to_array(img)\n",
    "x = np.expand_dims(x, axis=0)\n",
    "x = preprocess_input(x)\n",
    "\n",
    "print(happyModel.predict(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5 - Other useful functions in Keras (Optional)\n",
    "\n",
    "Two other basic features of Keras that you'll find useful are:\n",
    "- `model.summary()`: prints the details of your layers in a table with the sizes of its inputs/outputs\n",
    "- `plot_model()`: plots your graph in a nice layout. You can even save it as \".png\" using SVG() if you'd like to share it on social media ;). It is saved in \"File\" then \"Open...\" in the upper bar of the notebook.\n",
    "\n",
    "Run the following code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         (None, 64, 64, 3)         0         \n",
      "_________________________________________________________________\n",
      "zero_padding2d_1 (ZeroPaddin (None, 70, 70, 3)         0         \n",
      "_________________________________________________________________\n",
      "conv0 (Conv2D)               (None, 64, 64, 32)        4736      \n",
      "_________________________________________________________________\n",
      "bn0 (BatchNormalization)     (None, 64, 64, 32)        128       \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 64, 64, 32)        0         \n",
      "_________________________________________________________________\n",
      "max_pool (MaxPooling2D)      (None, 32, 32, 32)        0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 32768)             0         \n",
      "_________________________________________________________________\n",
      "fc (Dense)                   (None, 1)                 32769     \n",
      "=================================================================\n",
      "Total params: 37,633\n",
      "Trainable params: 37,569\n",
      "Non-trainable params: 64\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "happyModel.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "Failed to import pydot. You must install pydot and graphviz for `pydotprint` to work.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32mD:\\ProgramData\\Anaconda3\\lib\\site-packages\\pydot.py\u001b[0m in \u001b[0;36mcreate\u001b[1;34m(self, prog, format)\u001b[0m\n\u001b[0;32m   1877\u001b[0m                 \u001b[0mshell\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1878\u001b[1;33m                 stderr=subprocess.PIPE, stdout=subprocess.PIPE)\n\u001b[0m\u001b[0;32m   1879\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\ProgramData\\Anaconda3\\lib\\subprocess.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, args, bufsize, executable, stdin, stdout, stderr, preexec_fn, close_fds, shell, cwd, env, universal_newlines, startupinfo, creationflags, restore_signals, start_new_session, pass_fds, encoding, errors)\u001b[0m\n\u001b[0;32m    706\u001b[0m                                 \u001b[0merrread\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrwrite\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 707\u001b[1;33m                                 restore_signals, start_new_session)\n\u001b[0m\u001b[0;32m    708\u001b[0m         \u001b[1;32mexcept\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\ProgramData\\Anaconda3\\lib\\subprocess.py\u001b[0m in \u001b[0;36m_execute_child\u001b[1;34m(self, args, executable, preexec_fn, close_fds, pass_fds, cwd, env, startupinfo, creationflags, shell, p2cread, p2cwrite, c2pread, c2pwrite, errread, errwrite, unused_restore_signals, unused_start_new_session)\u001b[0m\n\u001b[0;32m    991\u001b[0m                                          \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfspath\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcwd\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mcwd\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 992\u001b[1;33m                                          startupinfo)\n\u001b[0m\u001b[0;32m    993\u001b[0m             \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [WinError 2] 系统找不到指定的文件。",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mException\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32mD:\\ProgramData\\Anaconda3\\lib\\site-packages\\keras\\utils\\vis_utils.py\u001b[0m in \u001b[0;36m_check_pydot\u001b[1;34m()\u001b[0m\n\u001b[0;32m     22\u001b[0m         \u001b[1;31m# to check the pydot/graphviz installation.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 23\u001b[1;33m         \u001b[0mpydot\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDot\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpydot\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     24\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\ProgramData\\Anaconda3\\lib\\site-packages\\pydot.py\u001b[0m in \u001b[0;36mcreate\u001b[1;34m(self, prog, format)\u001b[0m\n\u001b[0;32m   1882\u001b[0m                     '\"{prog}\" not found in path.'.format(\n\u001b[1;32m-> 1883\u001b[1;33m                         prog=prog))\n\u001b[0m\u001b[0;32m   1884\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mException\u001b[0m: \"dot.exe\" not found in path.",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-12-00bc26b0b649>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mplot_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhappyModel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mto_file\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'HappyModel.png'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mSVG\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel_to_dot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhappyModel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprog\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'dot'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'svg'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\ProgramData\\Anaconda3\\lib\\site-packages\\keras\\utils\\vis_utils.py\u001b[0m in \u001b[0;36mplot_model\u001b[1;34m(model, to_file, show_shapes, show_layer_names, rankdir)\u001b[0m\n\u001b[0;32m    129\u001b[0m             \u001b[1;34m'LR'\u001b[0m \u001b[0mcreates\u001b[0m \u001b[0ma\u001b[0m \u001b[0mhorizontal\u001b[0m \u001b[0mplot\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    130\u001b[0m     \"\"\"\n\u001b[1;32m--> 131\u001b[1;33m     \u001b[0mdot\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel_to_dot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshow_shapes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshow_layer_names\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrankdir\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    132\u001b[0m     \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mextension\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplitext\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mto_file\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    133\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mextension\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\ProgramData\\Anaconda3\\lib\\site-packages\\keras\\utils\\vis_utils.py\u001b[0m in \u001b[0;36mmodel_to_dot\u001b[1;34m(model, show_shapes, show_layer_names, rankdir)\u001b[0m\n\u001b[0;32m     50\u001b[0m     \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodels\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mSequential\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     51\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 52\u001b[1;33m     \u001b[0m_check_pydot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     53\u001b[0m     \u001b[0mdot\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpydot\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     54\u001b[0m     \u001b[0mdot\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'rankdir'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrankdir\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\ProgramData\\Anaconda3\\lib\\site-packages\\keras\\utils\\vis_utils.py\u001b[0m in \u001b[0;36m_check_pydot\u001b[1;34m()\u001b[0m\n\u001b[0;32m     25\u001b[0m         \u001b[1;31m# pydot raises a generic Exception here,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     26\u001b[0m         \u001b[1;31m# so no specific class can be caught.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 27\u001b[1;33m         raise ImportError('Failed to import pydot. You must install pydot'\n\u001b[0m\u001b[0;32m     28\u001b[0m                           ' and graphviz for `pydotprint` to work.')\n\u001b[0;32m     29\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mImportError\u001b[0m: Failed to import pydot. You must install pydot and graphviz for `pydotprint` to work."
     ]
    }
   ],
   "source": [
    "plot_model(happyModel, to_file='HappyModel.png')\n",
    "SVG(model_to_dot(happyModel).create(prog='dot', format='svg'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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